Regression models for demand reduction based on cluster analysis of load profiles

Yamaguchi, Nobuyuki; Han, Junqiao; Ghatikar, Girish; Kiliccote, Sila; Piette, Mary Ann; Asano, Hiroshi

doi:10.1109/sae.2009.5534840

Cited by 18 publications

(6 citation statements)

References 8 publications

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“…In many markets, the calculation of customer base load (CBL) is based on the average consumption of customers in the last 2, 4, 5, 8, or 10 similar days (Faria et al, 2013;Yamaguchi et al, 2009). In this study, we use the average consumption of last 5 similar days.…”

Section: Proposed Methods For Calculating Cblmentioning

confidence: 99%

Customer baseline load models for residential sector in a smart-grid environment

2016

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Demand side management (DSM) Customer baseline load (CBL) Building load coefficient (BLC) Building insulation Flexible load (FL) Non-flexible load (NFL) a b s t r a c tDemand response (DR) can expand the customer participation in the electricity market and lead by changing its pattern from a simple function to an interactive relation. There are various methods to evaluate the successful implementation of DR program, the most important of which is determination of customer baseline load (CBL). In fact, CBL is the expected pattern of customer consumption in the absence of DR programs. Few works have been done in the field of calculation of CBL in residential sector, while most of them have paid little attention to the impact of changes in weather conditions on these calculations.In this paper, a new method is presented for the calculation of CBL for customers in residential sector in the context of a smart grid, considering the impact of weather changes. The results clearly show the high impact of changes in weather conditions on the calculation of CBL, and also show the extent of effect of buildings' improved insulation on this parameter. It is also indicated that implementing DR programs can increase the willingness of customers in residential sector to improve the insulations of their buildings.

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Section: Proposed Methods For Calculating Cblmentioning

confidence: 99%

Customer baseline load models for residential sector in a smart-grid environment

2016

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“…Regression models for demand reduction of DR programs and screening for recruiting customer enrollment into the program is employed by Yamaguchi et al . (). They used the cluster analysis of load profiles.…”

Section: Demand Response In the Electricity Marketmentioning

confidence: 97%

“…In a study based on regression models for demand reduction, Yamaguchi et al . () used the cluster analysis of load profiles. Customer clustering methods include: modified follow‐the‐leader, hierarchical clustering, k‐ means and fuzzy k ‐means algorithm based partitioning clustering and Kohonen self‐organizing map.…”

Section: Customer Base‐linementioning

confidence: 99%

Demand, Customer Base‐line and Demand Response in the Electricity Market: A Survey

Heshmati

2013

Journal of Economic Surveys

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The increasing use of demand‐side management, as a tool to reliably meet electricity demands at peak time, has stimulated interest among researchers, consumers and producer organiza‐tions, managers, regulators and policymakers. This research reviews the growing literature on models which are used to study demand, customer base‐line (CBL) and demand response in the electricity market. After characterizing the general demand models, the CBL, based on which the demand response models are studied, is reviewed. Given the experience gained from the review and existing conditions, the study combines an appropriate model for each case for a possible application to the electricity market; moreover, it discusses the implications of the results. In the literature, these aspects are studied independently. The main contribution of this survey is attributed to the treatment of the three issues as sequentially interdependent. The review is expected to enhance the understanding of the demand, CBL and demand response in the electricity market and their relationships. The objective is conducted through a combination of demand and supply side managements in order to reduce demand through different demand response programs during peak times. This enables electricity suppliers to save costly electricity generation and at the same time reduce energy vulnerability.

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“…The calculation methods of CBL can be classified into two categories: the statistical methods [5] and the regression methods [6]- [8]. For statistical methods, the predicted baseline load (PBL) is calculated by simply averaging the load of the selected prior days.…”

Section: Introductionmentioning

confidence: 99%

“…In [10], an hourly average was performed based on historical data, the resulting load profile was normalized (the load values were divide by the largest load value), and the CBL was derived from the modified normalized baseline load profile which was scaled up by multiplication with the load value of the hour prior to the event. A cluster analysis based baseline load calculation model was presented in [6].These methods all improve the calculation accuracy to some extent, but calculation error still cannot be completely eliminated. Therefore, error estimation needs to be carried out.…”

Section: Introductionmentioning

confidence: 99%

Error estimation of statistical baseline load methods

Miao

Zhang

Han

2015

2015 IEEE Power &Amp; Energy Society General Meeting

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Customer baseline load (CBL) is widely used as an evaluation tool in demand response (DR) programs. Due to uncertainty of load, it is difficult to establish an accurate CBL model and the calculation error is inevitable. Therefore, calculation error of CBL must be estimated, so that DR programs can be evaluated effectively. Consumers' load changes with periodic pattern of production and life, meanwhile, it is also affected by some random factors. To reveal the relevance between CBL and the influence brought by the periodic trend and random factors of the load, an analysis is done in the paper. Firstly, discrete Fourier transform (DFT) is used to decompose the historical load into different periodic components. Then error of CBL calculation methods is analyzed. It can be seen that periodic components have a close relationship with the error limit of each method and give valuable information to determine the error range. Case studies demonstrate the validity of the analysis.

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Regression models for demand reduction based on cluster analysis of load profiles

Cited by 18 publications

References 8 publications

Customer baseline load models for residential sector in a smart-grid environment

Customer baseline load models for residential sector in a smart-grid environment

Demand, Customer Base‐line and Demand Response in the Electricity Market: A Survey

Error estimation of statistical baseline load methods

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